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Improved results on state estimation for neural networks with time-varying delays
  • 时间：0
  • 分类：TP13[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
  • 相关基金：supported by the National Natural Science Foundation of China (No.60764001, 60835001, 60875035)
  • 相关项目：前馈神经网络的奇异学习动态研究
关键词： EXPONENTIAL, state, ESTIMATOR, RECURRENT, neural, networks, EXPONENTIAL, stability, TIME-VARYING, DELAYS, Linear, matrix, inequality, (LMI), Exponential state estimator, Recurrent neural networks, Exponential stability, Time-varying delays, Linear matrix inequality (LMI)
中文摘要：

In this paper, some improved results on the state estimation problem for recurrent neural networks with both time-varying and distributed time-varying delays are presented. Through available output measurements, an improved delay-dependent criterion is established to estimate the neuron states such that the dynamics of the estimation error is globally exponentially stable, and the derivative of time-delay being less than 1 is removed, which generalize the existent methods. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed results.

英文摘要：

In this paper, some improved results on the state estimation problem for recurrent neural networks with both time-varying and distributed time-varying delays are presented. Through available output measurements, an improved delay-dependent criterion is established to estimate the neuron states such that the dynamics of the estimation error is globally exponentially stable, and the derivative of time-delay being less than 1 is removed, which generalize the existent methods. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed results.

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